mlsanityData to 14 Oct 2026

The Probabilistic Model Checker Storm

arXiv 2002.07080 ↗First posted 17 Feb 2020cs.SEcited by 66

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Abstract (arXiv)

We present the probabilistic model checker Storm. Storm supports the analysis of discrete- and continuous-time variants of both Markov chains and Markov decision processes. Storm has three major distinguishing features. It supports multiple input languages for Markov models, including the JANI and PRISM modeling languages, dynamic fault trees, generalized stochastic Petri nets, and the probabilistic guarded command language. It has a modular set-up in which solvers and symbolic engines can easily be exchanged. Its Python API allows for rapid prototyping by encapsulating Storm's fast and scalable algorithms. This paper reports on the main features of Storm and explains how to effectively use them. A description is provided of the main distinguishing functionalities of Storm. Finally, an empirical evaluation of different configurations of Storm on the QComp 2019 benchmark set is presented.

Key facts. Cited by 66 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Feb 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 9; 2023: 11; 2024: 13; 2025: 14; 2026: 15.

66 papers.

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  1. 2022Safe Reinforcement Learning via Shielding under Partial Observabilitycited by 56
  2. 2023Closed-loop Analysis of Vision-based Autonomous Systems: A Case Studycited by 18
  3. 2024Robust Markov Decision Processes: A Place Where AI and Formal Methods Meetcited by 18
  4. 2022LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement Learningcited by 11
  5. 2023Solving Long-run Average Reward Robust MDPs via Stochastic Gamescited by 10
  6. 2025We'll Fix it in Post: Improving Text-to-Video Generation with Neuro-Symbolic Feedbackcited by 10
  7. 2021Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes (Technical Report)cited by 9
  8. 2024Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policiescited by 9
  9. 2024Neuro-Symbolic Evaluation of Text-to-Video Models using Formal Verificationcited by 9
  10. 2025NeuS-QA: Grounding Long-Form Video Understanding in Temporal Logic and Neuro-Symbolic Reasoningcited by 8
  11. 2024Pessimistic Iterative Planning with RNNs for Robust POMDPscited by 7
  12. 2022Abstraction-Refinement for Hierarchical Probabilistic Modelscited by 6
38 more
  1. 2024Unifying Qualitative and Quantitative Safety Verification of DNN-Controlled Systemscited by 6
  2. 2024Enhancing RL Safety with Counterfactual LLM Reasoningcited by 6
  3. 2022Under-Approximating Expected Total Rewards in POMDPscited by 5
  4. 2022Discrete-Event Controller Synthesis for Autonomous Systems with Deep-Learning Perception Componentscited by 5
  5. 2022Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checkingcited by 5
  6. 2024Probabilistic Model Checking of Stochastic Reinforcement Learning Policiescited by 5
  7. 20241-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalizationcited by 5
  8. 2024Learning Explainable and Better Performing Representations of POMDP Strategiescited by 4
  9. 2024Playing Games with your PET: Extending the Partial Exploration Tool to Stochastic Gamescited by 4
  10. 2021Leaving Goals on the Pitch: Evaluating Decision Making in Soccercited by 3
  11. 2025Turn-based Multi-Agent Reinforcement Learning Model Checkingcited by 3
  12. 2025Scenario-based Compositional Verification of Autonomous Systems with Neural Perceptioncited by 3
  13. 2026Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MCcited by 3
  14. 2021Task-Guided Inverse Reinforcement Learning Under Partial Informationcited by 2
  15. 2023Efficient Sensitivity Analysis for Parametric Robust Markov Chainscited by 2
  16. 2023Graph-Based Reductions for Parametric and Weighted MDPscited by 2
  17. 2023Analyzing Intentional Behavior in Autonomous Agents under Uncertaintycited by 2
  18. 2023Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guaranteescited by 2
  19. 2024What should be observed for optimal reward in POMDPs?cited by 2
  20. 2025Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning Policiescited by 2
  21. 2025Compositional shield synthesis for safe reinforcement learning in partial observabilitycited by 2
  22. 2025Automated Generation of MDPs Using Logic Programming and LLMs for Robotic Applicationscited by 2
  23. 2026RT-NeuS: Towards Real-Time Neuro-Symbolic Video Understanding via Adaptive Temporal Verificationcited by 2
  24. 2026Shields to Guarantee Probabilistic Safety in MDPscited by 2
  25. 2022Task-Guided IRL in POMDPs that Scalescited by 1
  26. 2023Deductive Controller Synthesis for Probabilistic Hyperpropertiescited by 1
  27. 2023Formally-Sharp DAgger for MCTS: Lower-Latency Monte Carlo Tree Search using Data Aggregation with Formal Methodscited by 1
  28. 2025Accelerating Policy Synthesis in Large-Scale MDPs via Hierarchical Adaptive Refinementcited by 1
  29. 2025Bounded PCTL Model Checking of Large Language Model Outputscited by 1
  30. 2025Verifying Memoryless Sequential Decision-making of Large Language Modelscited by 1
  31. 2026Easy-to-Use Shielding for Reinforcement Learningcited by 1
  32. 2021Efficient Strategy Synthesis for MDPs with Resource Constraintscited by 0
  33. 2022On the Verification of Belief Programscited by 0
  34. 2022Formally Verified Solution Methods for Infinite-Horizon Markov Decision Processescited by 0
  35. 2023Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guaranteescited by 0
  36. 2023Finding an ε-close Variation of Parameters in Bayesian Networkscited by 0
  37. 2023Improving Probabilistic Bisimulation for MDPs Using Machine Learningcited by 0
  38. 2024Expectation vs. Reality: Towards Verification of Psychological Gamescited by 0

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